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Vol. 19, No. 3, September 2008, pp. 273–290
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The Effects of the Social Structure of Digital
Networks on Viral Marketing Performance
Mauro Bampo
School of Information Technology, Monash University, Melbourne, Australia,
[email protected]
Michael T. Ewing
Department of Marketing, Monash University, Melbourne, Australia,
[email protected]
Dineli R. Mather
School of Engineering and Information Technology, Deakin University, Melbourne, Australia,
[email protected]
David Stewart
Department of Marketing, Monash University, Melbourne, Australia,
[email protected]
Mark Wallace
School of Information Technology, Monash University, Melbourne, Australia,
[email protected]
V
iral marketing is a form of peer-to-peer communication in which individuals are encouraged to pass on
promotional messages within their social networks. Conventional wisdom holds that the viral marketing
process is both random and unmanageable. In this paper, we deconstruct the process and investigate the formation of the activated digital network as distinct from the underlying social network. We then consider the
impact of the social structure of digital networks (random, scale free, and small world) and of the transmission
behavior of individuals on campaign performance. Specifically, we identify alternative social network models to
understand the mediating effects of the social structures of these models on viral marketing campaigns. Next,
we analyse an actual viral marketing campaign and use the empirical data to develop and validate a computer
simulation model for viral marketing. Finally, we conduct a number of simulation experiments to predict the
spread of a viral message within different types of social network structures under different assumptions and
scenarios. Our findings confirm that the social structure of digital networks play a critical role in the spread
of a viral message. Managers seeking to optimize campaign performance should give consideration to these
findings before designing and implementing viral marketing campaigns. We also demonstrate how a simulation
model is used to quantify the impact of campaign management inputs and how these learnings can support
managerial decision making.
Key words: digital communication; social structure of digital networks; viral marketing
History: Anil Gupta, Senior Editor. This paper was received on July 14, 2006, and was with the authors
5 months for 2 revisions. Published online in Articles in Advance June 5, 2008.
1.
Introduction
(C2C), or “peer-to-peer” (P2P) communication as well
as “buzz marketing” have also been variously associated with the process. The term “buzz,” almost by
definition, has an ephemeral connotation. We concur with Dobele et al. (2005) and view “buzz” as
an output or consequence of viral marketing. The
viral metaphor neatly captures the essence of the
communications process and draws on a rich body
The term “viral marketing” appears to have first been
coined by venture capitalist Steve Jurvetson in 1996 to
describe the marketing strategy of free e-mail service
Hotmail (Kaikati and Kaikati 2004). Since then, contemporary business literature has become somewhat
enamoured with the concept. Terms such as “wordof-web,” “word-of-mouse,” “customer-to-customer”
273
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Bampo et al.: The Effects of the Social Structure of Digital Networks on Viral Marketing Performance
of literature in medicine and the health sciences (e.g.,
Mather and Crofts 1999, Mather 2000).
Viral marketing broadly describes any strategy
that encourages individuals to propagate a message,
thus creating the potential for exponential growth in
the message’s exposure and influence. Kaikati and
Kaikati (2004) view it as “ word of mouth via a digital platform spreading the message via ‘word of
mouse’ and ensuring that the receivers have the interest to pass along the message to their acquaintances.”
Similarly, Dobele et al. (2005) describe it as “encouraging individuals to pass on messages received in
a hypermedia environment, such as e-mail or other
messaging system.”
Viral approaches have numerous advantages over
more traditional mass media. For example, there is
a natural selection process embedded in the way
the message is propagated. This reduces redundancy
in the sense that communication is more targeted.
Other advantages include speed of diffusion and a
reduced likelihood for the message to be altered
by senders (in other words, a high degree of message integrity). And, if the message has an embedded call to action, then the conversion rate (i.e.,
behavioral response) is potentially more quantifiable
than in other forms of mass communication. Viral
communication also affords the marketer a greater
degree of creative license through a message delivery medium that is more intimate and personalized,
thereby increasing the likelihood of reaching “hardto-get” audience members.
The viral process can be broadly modelled in terms
of three components: the social structure of the digital
network through which the message is propagated,
the behavioral characteristics of its members that facilitate the propagation of the message, and a seeding
strategy that initiates the process. This study is based
on the model introduced by Stewart et al. (2004),
where the underlying social network is represented
by a random graph and network members’ behavior
is defined by the susceptible-infective-removed (SIR)
pattern from epidemic theory (Becker 1989).
The objectives of the study are threefold: first,
to identify alternative social network models and
to understand the mediating effects of social structures of these models on viral marketing campaigns;
second, to develop a process for modelling viral
Information Systems Research 19(3), pp. 273–290, © 2008 INFORMS
marketing campaigns and empirically validate the
ensuing activated digital network model; and third,
to conduct a number of simulation experiments to
explore the influence of the various controlled and
external factors on viral campaigns.
The article is set out as follows. First, we examine the viral marketing process and define various
campaign performance metrics. We then model different social structures as digital networks and define
parameters which describe the network and control
the spread of the viral message. Next, we introduce
empirical data from a recent viral marketing campaign carried out by a leading automotive manufacturer, and develop a computer simulation model. We
then describe a number of simulations which predict
the spread of a viral message within different kinds
of social networks under different assumptions about
the network itself, the behavioral characteristics of its
members, and the seeding strategy that initiates the
process. Finally, we draw conclusions about the mediating effect of the social structure of digital networks
on campaign performance as well as the extent of the
control available to campaign managers.
2.
Literature Review
The transition from traditional word-of-mouth networks to digital networks has greatly expanded
the opportunities for bidirectional communication
(Dellarocas 2003) and, in the process, created a pervasive and intriguing phenomenon (Goldenberg et al.
2001) that has piqued the attention of researchers
from diverse disciplinary backgrounds. In reviewing
this rich body of cross-disciplinary literature, two
emerging streams are discernable: namely, a behavioral stream (incorporating advertising and marketing) and a management science stream—with strong
foundations in information systems and operations
research.
The behavioral stream has focused on characteristics, motivations, and reported behaviors of customers
and the extent to which these might influence the success of viral marketing campaigns. This has included
surveys of intended purchasing behaviors (Gruen
et al. 2006) as well as examining the interactions
between customer and product characteristics (Helm
2000) and their effects on message transmission.
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Findings across the board in this research stream
suggest that inherent customer heterogeneity warrants highly segmented viral campaigns to address
individual customer differences and preferences. Message customization and social network status are two
salient antecedents to determining the “spread” of the
communication (Phelps et al. 2004, Podoshen 2006).
A third key determinant is customer motivations
(Gelb and Sundaram 2002, Phelps et al. 2004), be they
intrinsic or extrinsic (e.g., reward programs, competitions, coupons).
The behavioral research stream is generally more
applied and is aimed at practising managers looking
to utilise digital social networks and online word-ofmouth in a more effective manner. Sophisticated targeting strategies are suggested and discussed (Dobele
et al. 2005) and much emphasis is placed on accurate
initial targeting (seeding) of customers (Phelps et al.
2004).
Despite the early progress made by the behavioral
researchers, progress in this area has been somewhat
limited by virtue of the character of viral marketing and word-of-mouth networks—there are ethical ramifications regarding consumer privacy (Phelps
et al. 2004) if researchers were to accurately track
and record data on all consumer interactions during
a particular viral campaign. Additionally, important
information as to why consumers propagate viral marketing messages, such as emotional engagement with
the message (Dobele et al. 2007) or why consumers
seek or provide an opinion for word-of-mouth networks (Goldsmith and Horowitz 2006), is difficult to
obtain without directly interviewing or surveying the
consumer. To do so invites the possibility of experimenter expectancy effects (Miller and Turnbull 1986,
Rosenthal 1994) and potentially influencing the natural activity of passing on the marketing message. Furthermore, given the uncontrollable and “explosive”
nature of the spread of viral marketing campaigns
and online word-of-mouth networks (Dobele et al.
2005), accurate sampling of the population reached by
a viral campaign is problematic.
More formalized studies are also needed to
progress beyond extrapolated knowledge gleaned
from (often modest) samples of customers (or students) to larger, more heterogeneous, “real world”
275
populations—in other words, to model actual behavior rather than intended or reported behavior (Gelb
and Sundaram 2002, Gruen et al. 2006, Helm 2000).
The majority of behavioral research has also been limited to snapshot studies (Ba and Pavlou 2002, Gruen
et al. 2006), with little opportunity for longitudinal
studies to gauge the full extent of viral marketing
campaigns on consumers in the natural setting. Smallsized samples (Weinberg and Davis 2005) and constrained populations from which samples have been
drawn (Phelps et al. 2004), limitations common to
research of this type, also reduce the generalisability of the behavioral research findings in this area. In
addition, theoretical explanations for how viral marketing functions (Phelps et al. 2004) would further
enhance understandings and applications of this area.
The management science stream, in contrast, has
focused more on the design aspects of specific
mechanisms (especially online reputation feedback
mechanisms) and on the potential for influencing performance through deliberate structural and design
manipulation. Using given systems parameters and
different theoretical approaches (such as game theory), this body of work exploits mathematical modelling approaches to studying online communication
networks. In particular, the work of Dellarocas (2003,
2005) illustrates how the design of a given system
(such as an eBay-like reputation mechanism) can
engender, support, and elicit certain responses from
customers rather than relying on customer-initiated
behavior. Other researchers in this stream have introduced trust into their models, not as a preexisting characteristic of the customer but as a construct
engendered by the system itself (Ba and Pavlou 2002,
Pavlou and Gefen 2004). Trust is crucial in this context given that the anonymity of online members and
lack of actual context (Dellarocas 2003) increases the
opportunity for online fraud (Bolton et al. 2004). The
building of online reputations has even been modelled as a capital asset that must be maintained and
invested in Rob and Fishman (2005).
This body of management science literature potentially provides a proactive approach to afford greater
control over the performance of online mechanisms.
The mathematical models are elegant and sophisticated in their execution and provide a solid framework (with given assumptions) on which knowledge
of online processes can be further developed.
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Bampo et al.: The Effects of the Social Structure of Digital Networks on Viral Marketing Performance
However, the focus of this body of work tends to
be on the characteristics of the systems, not the characteristics and behaviors of the customers. As such,
many are limited by parameter assumptions that are
not replicated in real-world applications. Characteristics and behaviors of the online consumers are often
unknown variables within mathematical models. A
range of consumer characteristics, for example, intrinsic personal motivations (Bolton et al. 2004) and socioeconomic status (Ba and Pavlou 2002, Gruen et al.
2002) are difficult to quantify in models, and behavior
that falls outside of model parameters is equally problematic (Dellarocas 2005). Additionally, some models looking at consumer-generated feedback assume
that feedback is truthful and not manipulated (Bolton
et al. 2004). Even when manipulation is factored as
a variable in Internet opinion forum models, there is
an underlying assumption that consumers are competent to gauge levels of manipulation by companies (Dellarocas 2006) to adjust their own behavior
accordingly. Research in this area has indicated a need
to further understand design and parameter implications, for developing responses such as trust in
consumers (Pavlou and Gefen 2004), and for controlling the behavior of users, for example, identity fraud
(Dellarocas 2003).
A third avenue of inquiry appears to be emerging
that has the potential to bridge the aforementioned
two literature streams. Mayzlin and her colleagues
(Chevalier and Mayzlin 2006, Godes and Mayzlin
2004, Mayzlin 2006) draw on both behavioral
and management science traditions and approach
customer-generated characteristics and behaviors as
constructs which can be utilised as known quantities in mathematical models. Godes and Mayzlin’s
(2004) work, in particular, applies real-world data to a
model to reveal which components of word-of-mouth
communication are most effective. Components such
as reach (Godes and Mayzlin 2004), quality of networks (Goldenberg et al. 2001), and quality of the
message or feedback (Chevalier and Mayzlin 2006)
can be modelled. Such information can potentially
assist managers in predicting the usefulness of a particular word-of-mouth strategy and the potential for
flow-on marketing to continue even after the initial
advertising has ended (Goldenberg et al. 2001).
Information Systems Research 19(3), pp. 273–290, © 2008 INFORMS
Notwithstanding the emerging bodies of literature
in this field, notable gaps still exist. In particular,
Dellarocas’ (2003) call for more research on feedback
mechanism design can be extended more broadly
to other aspects of viral marketing/P2P communication, including theory-driven experimental and
empirical research that explores the global impact of
buyer-seller behavior and a better understanding of
how managers must adapt their strategies in online
contexts. Our study builds on Mayzlin’s foundations
and goes some way toward addressing Dellarocas’
call. In so doing, it aims to assist firms to develop
more formalised and sophisticated approaches to
viral marketing (Helm 2000).
3.
Deconstructing the Viral Process
We deconstruct the viral marketing process into the
following components: underlying population and
their social connectivity; the campaign characteristics; the behavioral characteristics of the audience that
facilitates the propagation of the message; the size and
connectivity of the successfully reached audience; and
measures of dynamic campaign progress. Specifically,
we model the size and connectivity of the population as a network, taking into account the campaign
characteristics. We then simulate the campaign, and
the campaign performance measures are reflected as
properties of the simulation.
3.1. The Structure of a Digital Network
A network is specified by a set of nodes and a set
of edges linking pairs of nodes. The nodes represent
members of the population, or audience, and the
edges represent communication links between them
that may be used to spread the viral message. The
degree of a node is the number of edges linking it to
other nodes. Two nodes are connected if there is a
sequence of edges forming a path from one node to
the other. Thus, a node with degree zero is not connected to any other node. The distance between two
connected nodes is the length of the shortest path connecting them.
Three properties of a network that will be used in
this paper are: (i) its number of nodes N , (ii) the average degree of its nodes , and (iii) the average distance
between pairs of nodes L. The parameter is a measure
of network connectedness.
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3.1.1. Random Networks. Stewart et al. (2004)
introduced a random viral marketing model (RVM)
based on the random network model developed by
Erdös and Rényi (1959) and described by Albert and
Barabási (2002). This digital network is represented
by a random graph and network members’ behavior is defined by the susceptible-infective-removed
sequence (Becker 1989). A random network can be
generated by starting with a set of isolated nodes and
allowing each of the N nodes to have a probability of being connected by an edge to each other node.
As noted by Albert and Barabási (2002), in a random network the degree of its nodes follows a binomial distribution with parameters N − 1 and . As
each node has the potential to connect up to N − 1
other nodes, on average we expect each node to be
connected to = N − 1 other nodes, resulting in
an expected total of 12 N links. In the context of viral
marketing, a typical network has large N and small resulting in the average degree remaining moderate.
The degree of a node therefore has an approximate
Poisson distribution with mean network√ connectedness . Because the standard deviation is , it is very
unlikely for a node to have degree of size comparable
with N . In other words, it is unlikely that any node is
directly linked to a significant proportion of the nodes
in the network.
3.1.2. Scale-Free Networks. Research into scalefree networks has proliferated since their introduction
by Barabási (1999) and Albert and Barabási (2002).
These networks provide useful representations of
many different self-organizing systems, ranging from
the World Wide Web to citation patterns in scientific
publications to the electrical power grid of western
United States. The defining characteristic of a scalefree network is in the shape of the probability distribution for the degree of each node, which determines the number of communication links or edges
emanating from each member. The degree is assumed
to follow a Power-law distribution, defined by P k ∝
k− with > 0, where P k denotes the probability
that a node is connected to k other nodes. This is a
“fat-tailed” distribution where, with increasing k, the
probabilities decline at a much slower rate than those
of the Poisson distribution which essentially underlies
the RVM model. The Power-law distribution allows
for a small number of nodes to be directly linked to
a significant proportion of the nodes in the network
while most nodes have few connections, thus keeping
the mean number of connections comparatively low.
These high degree nodes, often called hubs, ensure
that the average distance L between any two nodes in
the network is small (independent of the size of the
network).
The scale-free network structure emerges naturally
as a consequence of two phenomena: dynamic growth
and preferential attachment (Barabási 1999, Albert
and Barabási 2002), both important features of social
networks. Where a network is created by adding new
members over time and these are connected to other
members with a probability that is proportional to
their connectivity, the resulting distribution for the
degree or number of connections per node will exhibit
a Power-law distribution. These structures are called
scale-free networks because despite their growth, they
preserve statistical properties such as the average distance L and the degree distribution.
Some studies (Dorogovtsev and Mendes 2003,
Drineas et al. 2004), based on analysis of the traffic on
SMTP servers, show that e-mail networks of limited
size (involving members of a single university) display properties of a scale-free network. e-mail graphs
were constructed in those studies, representing e-mail
addresses by nodes and adding a communication link
between each pair of nodes where at least one message had passed between them. Both the number of
incoming and outgoing links have been shown to follow a Power-law distribution. This feature of e-mail
graphs makes scale-free networks particularly interesting from a viral marketing perspective. To create a
scale-free graph, nodes are added one by one to the
network; every new node is linked by an undirected
arc to a preexisting node l with probability
P l =
d
· l 2
m dm
dl being the degree of node l, {m} the set of previously added nodes, and the desired average degree
of the network. Theoretical models of the spread of
diseases and the absence of epidemic thresholds in
scale-free networks are discussed at length in Boguna
et al. (2003), Eguıluz and Klemm (2002), Moreno and
Vázquez (2003), and Pastor-Satorras and Vespignani
(2001).
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3.1.3. Small World Networks. Small world graphs
were first introduced by Watts and Strogatz (1998) to
model a class of social networks characterized by high
clustering and short average distance between nodes.
Clustering is a local property of the network and is a
measure of the connectivity of a neighbourhood. The
clustering coefficient C of a node is defined as the fraction of the node’s neighbours that are linked to each
other. High clustering and long average distances are
typical features of lattice networks (Dorogovtsev and
Mendes 2003), where nodes can be thought of as
points in a multidimensional space and nearby points
are linked by edges. In contrast, small world networks
have short average distances between nodes.
Small world networks can be constructed from
lattice networks by applying a rewiring procedure:
arcs connecting neighbours (within the clusters) are
removed from the graph with probability rewiring
probability r and substituted by random links (making connections outside of the cluster). As r increases,
the average distance L decreases very quickly (Watts
and Strogatz 1998), producing a graph structure characterized by low node separation typical of random
networks and strongly connected neighbourhoods of
regular networks. With increasing r, the graph starts
to become more like a random network. The transition, however, is smooth and the evolution of the
average distance and level of clustering is also influenced by N (Barthélémy and Amaral 1999).
Small world networks are also potentially applicable to viral marketing because they capture the
connections generated through physical proximity.
Tightly linked neighbourhoods reflect social structures based on friendship or professional relationships
which are likely to form among people who interact
within a confined physical environment. For example, Albert and Barabási (2002) refer to a social system
where people are well-connected with their neighbours and work colleagues but also have a much
smaller number of connections with people who live
far away, in another state or country. Random links
represent the distant acquaintances and are useful
in representing connections between local networks.
A higher level of rewiring makes the viral message
spread faster and thus saturates the network sooner.
Information Systems Research 19(3), pp. 273–290, © 2008 INFORMS
3.2. Campaign Characteristics
The impact of a viral marketing campaign can be
influenced by the message attractiveness, the campaign design, and any intervention strategies. The
attractiveness or perceived value of a viral message as
well as offering an incentive (if any) play an important role in determining a recipient’s propensity to
forward the communication as well as which communication links to activate from within their digital
network connections. The campaign manager determines the number of seeds used, with seeding typically taking place at the start of a campaign. Once a
campaign is in progress, there are a number of ways
in which a campaign manager can track its progress.
For example, if the campaign includes a call to action
such as an online coupon or uses a Web interface as a
registration process, it is possible to identify the signs
of a flagging campaign and take corrective action to
resuscitate it.
3.3. Modelling the Propagation of a Viral Message
The behavior of network nodes determines the propagation of the message through the network. Network
propagation is modelled on a discrete time basis. Propagation along network edges occurs simultaneously at
each time instant. Using the SIR sequence nodes can
be in three states: (i) S—susceptible; (ii) I—infective;
and (iii) R—removed (or “immune”) and at any given
time, the total number of nodes N = S + I + R. Each
node is in a “susceptible” state before receiving the
message. On receiving the message, a node becomes
infected and remains “infective” for one time period
when it may propagate (forward) the message along
any of its edges according to a probability pF (sampled from a probability distribution) which we refer
to as the forwarding parameter. This is analogous to
the contagion parameter in epidemic theory (Becker
1989), as in a digital context the contagion parameter refers to the probability of forwarding a message.
After that time period, the node becomes “immune”
to the message (removed) and takes no further part
in the propagation process. Thus, we assume that any
further messages reaching the node are ignored in the
SIR sequence, as described by Moreno and Vázquez
(2003).
We employ the concept of a generation G (Stewart
et al. 2004) to identify the nodes reached by the message at each time instant, with the seeds forming the
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initial (zeroth) generation of the network members
reached. Hence, the generation can be used to index
the sequence of transmissions. Under the SIR pattern,
nodes forming each generation are the only infective ones.
3.4. The Activated Digital Network
Digital networks are best understood by considering the formation of digital connections as illustrated
in Figure 1. We start with the complete social network
which encapsulates all types of connections between
the nodes; for example, family ties or social or professional connections. Some of these connections are
digital or electronic and we refer to this “digital subset” of the complete network as the underlying digital
social network. When an electronic message is received
by an individual (node) within the underlying digital
social network, he or she is faced with two decisions:
first, whether or not to forward the message, according to the forwarding parameter pF ; and second, if he
or she decides to forward the message, then choosing
which existing digital links to activate. This latter process results in the creation of the activated digital network and is captured by an additional measure which
we define as the activation parameter pA . The parameters pF and pA are treated as stochastic in nature. This
process is represented in Figure 1.
3.5. Performance Measures of a Campaign
Stewart et al. (2004) defines three output measures
for viral campaigns: the process duration Ti ; the number of network members reached at the tth generation
Xi t; and the cumulative number reached up to and
including the tth generation Yi t. The index i denotes
the number of seeds (1 ≤ i < N ) used in the campaign.
Figure 1
A campaign is said to naturally terminate when
there are no longer any infectives. In this study, we
use two performance measures for a viral campaign:
the final reach or penetration, i.e., the proportion of the
target audience that has received the communication
by the time the process dies, and the length of the campaign, i.e., the number of generations required to reach
a predetermined proportion of the target audience.
Stewart et al. (2004) show that the main parameter influencing the spread of the viral message
is the epidemic threshold parameter (ETP) (Becker
1989), which measures the growth rate of infectives.
The ETP is defined as the product of the network
connectedness , the activation parameter pA , and the
forwarding parameter pF (i.e., = pF pA ). In the early
generations, the growth of the digital network is governed by the size of . When is significantly greater
than one, the message is being forwarded, on average,
to more than one individual and, hence, the number
of infectives Xi t grows at an exponential rate during
the earlier generations. Borrowing terminology from
the theory of branching processes (Becker 1989), we
say the digital network exhibits supercritical growth.
As the network becomes saturated (the proportion
of removals increases), the growth rate is reduced
to the point where it transmutes into an exponential
decay. An important property of a supercritical network is that the eventual reach (penetration) rapidly
approaches 100% of connected members and this is
governed primarily by the ETP. In this case, the number of seeds used is important to the extent of ensuring that the propagation process does not terminate
in the initial generations, but beyond that plays no
role in determining the eventual reach. On the other
hand, when is not significantly greater than one, the
Formation of an Activated Digital Network for Generations 1–2 Initiated by Four Seeds
Complete social network
279
Underlying digital network
(N, θ; λ)
Activated digital network
(N ′, pF , pA)
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network exhibits subcritical growth, where the number
of infectives Xi t decays at an exponential rate almost
from the beginning of the campaign. In the subcritical case, the number of seeds play an important role
as a higher penetration can be achieved by seeding a
wider proportion of the audience.
4.
Design of the Simulation Model
and Empirical Validation
4.1. Results from an Empirical Campaign
We analyse a viral marketing campaign conducted by
a leading automotive manufacturer, General Motors
Holden in Australia, which was considered to be
highly successful. In its use of seeding and the propagation of the marketing message through digital links
within the population, it was a typical digital marketing campaign. We simulate this campaign using
alternative network models and match their results
against the real campaign data. These results then
enable us to validate the simulation model and infer
characteristics of the real campaign. The knowledge
of such characteristics can be used to help manage
future campaigns.
In this digital automotive campaign, almost 39,000
self-selected target audience members were seeded
with promotional information about a new product
in the form of an e-brochure and were invited to
provide e-mail addresses of contacts who might be
interested in receiving the brochure themselves. The
Table 1
Gen.
t
0
1
2
3
4
5
6
7
8
9
10
11
12
13
company used the prize of a holiday with the likelihood of winning linked to the number of contacts
nominated as an incentive. The campaign eventually
reached an additional 43,000 people. Although the
estimated target market (N ) for the particular automotive model is approximately 171,000, it is unrealistic to assume that the campaign remained within the
bounds of this group and, hence, the percentage reach
that is implied by the numbers is very unlikely to be
accurate.
The initial viral seeding (i.e., generation 0) was
to 38,668 potential customers. Of these, 10,244 registered and generated at least 1 self-initiated outbound e-mail, and the total number of e-mails sent
to the next generation (generation 1) was 26,548. Of
these, 3,091 registered online and generated 9,089 new
contacts (generation 2) in total. And of these, 1,221
went on to register and generated 3,858 new e-mails
(i.e., generation 3). This process continued to the 13th
generation. Table 1 presents the campaign statistics by
generation t: the number of infectives who registered
(nt ); calculated parameter values for the estimated
probability of forwarding (that is, registering on the
campaign Web site and providing e-mail addresses of
their contacts) (pF t ), the estimated average number
of activated contacts per registered infective (pA t t ),
and the growth rate of infectives (t ) at each generation. The number of people receiving the message in
the later generations (7–13) was insufficient to make
reasonable estimates.
Campaign Statistics by Generation t
No. of
susceptibles
St
No. of
infectives
It
Cumulative no.
of removals
Rt
No. of infectives
who registered
nt
Estimated prob.
of forwarding
pF t = nt /It (%)
Estimated average no. of activated
contacts per registered infective
pA t t = It+1 /nt
Estimated growth
rate of infectives
t = It+1 /It
132339
105791
96702
92844
91039
90188
89745
89527
89406
89326
89294
89279
89271
89265
38661
26548
9089
3858
1805
851
443
218
121
80
32
15
8
6
0
38661
65209
74298
78156
79961
80812
81255
81473
81594
81674
81706
81721
81729
10244
3091
1221
564
279
147
72
39
20
12
6
2
2
265
116
134
146
155
173
163
26
29
32
32
31
30
30
07
03
04
05
05
05
05
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4.2. Network Model of the Campaign
The campaign data contains the communication links
that were activated as the message was transmitted
through the digital network. While it is not possible
to identify the totality of the underlying social or digital network, the realised or activated digital network
links are identified uniquely by the data. Closer analysis of this activated network shows while most of
the individuals have moderate to low (less than 10)
number of contacts, a small number of the audience
members (less than 0.03%) forwarded the message
to a significantly large number of people (more than
20 contacts, with a very small number forwarding to
over 100). In other words, there is some semblance
of a small number of large hubs (suggestive of scalefreeness). The data also shows that a majority of message forwarding takes place within the same state,
but a significant minority (10%) of links are activated
across different Australian states. While this provides
some preliminary evidence of small world characteristics with a high level of local clustering and a small
proportion of “long distance” connections, given the
large geographic regions covered by the Australian
states, inferring local clustering based on being in the
same state may be questionable.
A generation-by-generation analysis of the campaign shows that the behavior of the seeds is very
different to that of the subsequent generations where
the observed parameters are reasonably consistent.
This variability is not surprising given that the seeds
were a self-selected group who had registered beforehand on the company’s Web site to receive product
news and promotional information and, therefore,
had already manifested some interest in the category
of products. (This would not be unusual for viral
marketing campaigns where typically mailing lists
are used to seed campaigns.) In contrast, the subsequent generations are less aligned to the promotion
and likely to be more homogenous in their behavior.
Surprisingly, the campaign data also shows that the
seeds, while being more likely to pass the message
on, on average nominate fewer contacts than those in
subsequent generations.
We observe another anomaly in the empirical data
in the distribution of the number of contacts provided
by those who register for the campaign. As the Web
interface was designed to display five textboxes at a
281
time (with a button to request a further page for listing five more contacts), the distribution of the number
of contacts is a periodic U-shaped distribution that
cycles on multiples of five. Hence, the Web interface
in the General Motors campaign discouraged users
from forwarding a message to a large number of people. Therefore, forwarding to 5 people was easy but
forwarding to 500 would have been an extremely long
and tedious operation.
Again, the seeds have a different distribution
(mean and shape), reinforcing the argument presented above that the seeds behave differently to the
latter generations. Closer examination of the underlying distribution for the number of contacts across
the campaign appears to provide a reasonable fit to
a Power-law distribution, signifying that the associated digital network will display scale freeness. However, analysis of the data shows that there are a very
small number of hubs in total and, given the decay
in the reach, there are very few hubs beyond the
first two generations. In light of the above discussion, the activated digital network of the campaign
appears to exhibit mixed characteristics (random,
small world, scale free) which perhaps on reflection
is not unexpected.
4.3. Simulation of the Campaign
Given that the empirical data displays some evidence
of social structure (both small world and scale-free
characteristics) in order to determine the model of
best fit, we consider all three network models discussed in the previous section. We develop a computer simulation that enables us to replicate this
digital marketing campaign within each of the social
network models. The computer program simulates
the following processes (illustrated in Figure 2):
1. The creation of the underlying digital network
based on the population size, the network model, and
connectivity parameters (stochastic) used;
2. The seeding of the campaign with the message
based on campaign strategy used; and
3. The transmission of the message through the
digital network, generation by generation, as potential communication links are activated based on the
probability of transmission and the distribution of the
number of links activated (both stochastic).
Figure 2
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The Processes Used in Simulating a Viral Marketing Campaign
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Network model used
(e.g., small world)
Campaign performance
measures
(e.g., reach and spread)
Creation of the underlying
social network
Connectivity parameters
(e.g., number of contacts)
Seeding the network with
the promotional message
Seeding strategy (e.g.,
number and distribution
of seeds used)
Transmission of message
through the activation of
digital links (generation of
the activated network)
Behavioral parameters
(e.g., prob. of forwarding
and to how many)
When running test simulations, we encountered
a significant number of redundant communications
where some people receive the message from more
than one source, resulting in early termination of
the simulated campaign. Unfortunately, as redundant
e-mails were not recorded in the actual campaign,
we are not able to validate this fact. However, we
were able to confirm mathematically that it would
not have been possible for a viral communication
to spread through a population of the magnitude
171,000 without having generated a significant number of redundancies. As mentioned in §4.1, it is unrealistic to assume that the campaign remained within
the bounds of the target population. Hence, it is
likely that the actual digital network through which
this promotional message was transmitted was much
larger. In the following simulations, we use a larger
network of size one million in order to represent a
more realistic (larger) target audience, generate a simulation corresponding to the length of the actual campaign, and maintain a low proportion of simulated
redundancies (5%). A reason for limiting it to one
million was a pragmatic consideration, taking into
account computer memory and time limitations.
The simulation model uses weighted averages of
the observed transmission parameters, the estimated
target audience, and number of seeds used in
the campaign within each of three network structures. The simulation model is validated using the
empirical data.
The initial results (base case) generated by the
computer simulation model for each of the network
models versus observed campaign data are shown
in Figure 3. The variance of the simulation outputs
is low for all the networks (coefficient of variation
less than 5%), particularly in the early stages of the
campaign. These initial simulations also show that
the scale-free network produces significantly different
results to those observed in the actual campaign, particularly with respect to a higher growth in generation 2. As discussed in §4.2, the small number of hubs
significantly diminishes the scale freeness of the activated digital network, and this would explain the lack
of fit with a simulated scale-free model. In the simulation, hubs that were not seeded are much more likely
to be connected to the seeds (given their high connectivity). This results in a high proportion of hubs in
the simulated first generation, which in turn creates a
surge in growth in the next generation.
In contrast to the scale-free network, the simulated
campaigns using the random and small world networks have a good likeness to the actual campaign,
Figure 3
Results from the Base Case Simulation for Each Network
Structure Compared to the Actual Data
Base case simulation
Growth
282
45,000
40,000
35,000
30,000
25,000
20,000
15,000
10,000
5,000
0
Actual
Random
Small world
Scale free
0
1
2
3
4
Generation
5
6
7
Bampo et al.: The Effects of the Social Structure of Digital Networks on Viral Marketing Performance
283
Model Enhancements to Improve the Fit to
Actual Campaign Results
A limitation of the base case model is that it does not
take into account the different transmission behavior
of the seeds, as discussed in §4.2 and as is also evident
from the results shown in Figure 3. In recognition of
the different behavior of the seeds, we modify our
model by dividing the campaign into two “stages,”
each with its own transmission parameters (different
forwarding probability and activation parameter but
same network structure). The first stage consists of
just the seeds and the contacts they send to (first generation), and the second stage starts with the first generation and extends to the end of the campaign. We
estimate the transmission parameters for each stage
from the empirical data (shown in Table 2).
As shown in Figures 4(a) and 4(b), the enhanced
(2-stage) simulation models produce a very good
match with the outputs from the actual campaign,
particularly in the early generations. The simulated
model that best fits the campaign data is the random network (lowest mean square error). However,
the small world network also provides a very good fit
to the results of the General Motors campaign. This is
not surprising as the rewiring probability used for the
small world network estimated from the campaign
data is relatively high and, therefore, this network
displays similar characteristics to a random network.
Again, contrary to earlier expectations, the scale-free
network produces the least fit. Further analysis of the
Figure 4
Generational Growth and Reach for the Enhanced 2-Stage
Model (Random, Small World, and Scale Free)
(a)
Growth
but these results differ from the outputs observed in
the actual campaign in two aspects: first, there is a
significant difference in the number of contacts the
message was sent to by the seeds (the reach at generation 1), where the real campaign registered a higher
value than the simulated cases; and second, there is
a difference in the eventual reach, where the simulated campaigns’ performances are not as good as the
actual campaign.
Comparison of simulated enhanced 2-stage model
against campaign
45,000
40,000
35,000
30,000
25,000
20,000
15,000
10,000
5,000
0
4.4.
Table 2
Estimated Transmission Parameters
Whole campaign
Stage 1 (generation 0–1)
Stage 2 (generation 1–13)
Est. pF
Est. pA Est. 0.192
0.265
0.126
2.82
2.59
3.02
0.527
0.687
0.382
Actual
Random
Small world
Scale free
0
1
2
3
4
5
6
7
Generation
(b)
Generational reach
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Comparison of simulated enhanced 2-stage model
against campaign
120,000
100,000
80,000
60,000
40,000
20,000
0
0
1
2
3
4
5
6
7
Generation
Notes. Parameter values: N = 1000000, i = 38661, r = 01, pF (seeds) =
0265, pF (nonseeds) = 0126, pA = 01; mean number of people sent to by
seeds = 259; mean number of people sent to by nonseeds = 302.
simulated scale-free network shows that it has a disproportionately high number of large hubs in comparison to the actual campaign. These large hubs are
responsible for creating a surge in the reach midway
through the campaign, which results in higher overall reach. In contrast, the reach within the random
and small world networks falls short of the actual
campaign (the eventual reach of the random model is
within 5% and the small world model is within 7.5%).
These results would indicate that the activated digital
network of the General Motors Holden viral marketing campaign is best captured by a random network.
This is not unexpected given that the earlier analysis of the structure of the network suggested mixed
characteristics.
The simulation output of the enhanced model also
has low variance (coefficient of variance less than 5%)
in the early stages of the campaign and becomes a
bit more variable in the latter generations (coefficient
of variance up to 15%), as expected. In general, it is
expected that the more complex networks (with more
parameters) would generate more variable simulated
output, and the results from this study are consistent
Bampo et al.: The Effects of the Social Structure of Digital Networks on Viral Marketing Performance
Information Systems Research 19(3), pp. 273–290, © 2008 INFORMS
5.
Figure 5(a)
200,000
150,000
100,000
50,000
0
Sensitivity Analysis
The input parameters a campaign manager can influence are: (i) the number of seeds used; (ii) the
probability of forwarding a message; and (iii) the
average number of activated contacts (from the total
set of contacts). We vary each of these parameters
one at a time (while keeping all other parameters
constant and in line with the actual campaign), and
simulate the spread of the campaign within each theoretical network model. We vary the parameters within
a range on either side of the observed value from the
actual campaign.
5.1. Varying the Number of Seeds
Figure 5(a) shows the sensitivity of the reach (at each
generation) to varying the number of seeds used from
10,000 to 50,000 to 100,000 (with a target population of
1 million) within all network models. In general, the
change in the average eventual reach is proportional
to the change in the number of seeds used, suggesting the relationship between the number of seeds and
reach is approximately linear. At first glance, this may
appear counterintuitive given the potentially exponential growth of viral propagation. However, with
campaigns exhibiting subcritical growth rate of transmission (as in the General Motors campaign), this
underscores the need to maximize initial seeding. The
relative ranking of the three network models is consistent with what was observed in earlier simulations,
with the curve for the scale-free network positioned
clearly above the other two network models. This
may be explained by the role of hubs created by the
preferential attachment. When the number of seeds
used is low, there is very little separation between the
average reach achieved within the random and small
world networks. The reasons for this are twofold:
first, as discussed earlier, the relatively high value
Generational Reach with Varying Numbers of Seeds
(All Networks)
Sensitivity analysis varying the number of seeds used
Eventual reach
with that premise. With the small world network, the
strong clustering tendency makes simulation results
especially sensitive to the progress in the earlier generations of the campaign, as the number of random
links that are activated by the early generations can
influence how quickly the viral message spreads in
the system. In scale-free networks, the inclusion or
exclusion of hubs at each generation can make a significant difference in the simulated growth.
0
1
2
3
4
5
6
8
7
9
10
Generation
R 10K
R 50K
R 100K
Figure 5(b)
SW 10K
SW 50K
SW 100K
SF 10K
SF 50K
SF 100K
Growth Rate with Varying Numbers of Seeds
(Random, Small World, Scale Free)
Sensitivity analysis varying the number of seeds used
Growth rate (seeds)
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284
2.5
Random
SW
SF
2.0
1.5
1.0
0.5
0
0
20,000
40,000
60,000
80,000
100,000
Number of seeds used
Notes. Parameter values: N = 1000000, r = 01, pF (seeds) = 0265, pF
(nonseeds) = 0126, pA = 01; mean number of people sent to by seeds =
259; mean number of people sent to by nonseeds = 302.
of the rewiring probability has resulted in the small
world network tending to mimic a random network;
and second, combined with the relatively low values used for the transmission parameters (forwarding
probability and number sent to), the differences in the
network structure play a less significant role.
Figure 5(b) shows the average growth rate of the
infectives as the number of seeds is increased within
each network model. For example, when 500 seeds
are used in a scale-free network model, the growth
rate starts at 2.9 (500 seeds pass the message onto a
further 1,452 people, achieving a total eventual reach
of 1,952 for the campaign). Hence, as expected, as the
number of seeds used increases, the impact of each
seed (on the eventual reach) decreases. Further, the
simulated campaigns show that when the number of
Bampo et al.: The Effects of the Social Structure of Digital Networks on Viral Marketing Performance
285
Varying the Probability of Forwarding
the Message
Figures 6(a)–6(c) show the sensitivity of the eventual
reach to variation in the mean probability of forwarding within different network models. This parameter has the effect of a “switch,” where an increased
likelihood of forwarding results in all the activated
contacts being added versus the incremental increase
resulting from changing the average number of contacts. Hence, the impact of an increase in this parameter is significant. As shown in the simulated results,
an increase in the mean probability from 0.5 to 0.6
results in an increase in reach of over 10,000. Comparison of Figures 6(a)–6(c) also shows that the increase
in the probability of forwarding has much more of an
impact at the earlier generations of the campaign in a
scale-free network.
Figure 7 illustrates the change in the simulated
eventual reach across all networks as the probability of forwarding is varied. This analysis shows that
when the probability of forwarding is low, the scalefree network produces the best reach, but when the
probability of forwarding is high, this network structure does not perform as well as the other two
networks. This is likely to be caused by the higher
number of isolated nodes in a scale-free network,
where the increased probability of forwarding can
not counteract the lack of links. Up to generation 5,
the networks show similar sensitivity to forwarding
probabilities (see Figures 6(a)–6(c)), but thereafter the
impact of the isolated nodes or groups of nodes shows
up and the scale-free sensitivity graph flattens out. As
shown in Figure 7, when the forwarding probability
is high, the small world network produces the best
reach.
5.3. Varying the Number of Activated Links
Figures 8(a) and 8(b) show the effect of a variation in the number of activated links. As expected,
an increased number of links leads to higher reach
and once again there is a marked difference when
Generational Reach with Varying Mean Probability of
Forwarding (Random Network)
Sensitivity analysis varying the mean probability
of forwarding within a random network
Reach
5.2.
Figure 6(a)
800,000
700,000
600,000
500,000
400,000
300,000
200,000
100,000
0
0.2
0.3
0.4
0
1
0.5
0.6
2
3
4
5
6
7
8
9
10
Generation
Notes. Parameter values: N = 1000000, i = 38661, pA = 01; mean number of people sent to by seeds = 259; mean number of people sent to by
nonseeds = 302.
Figure 6(b)
Generational Reach with Varying Mean Probability of
Forwarding (Scale-Free Network)
Sensitivity analysis varying mean probability
of forwarding within a scale-free network
800,000
0.2
0.3
0.4
600,000
Reach
seeds used is high (10% of the total population), there
is little difference between the network models as the
message spreads very quickly (and easily) at the early
stages of the campaign and the intricacies of the network structure have less opportunity to play a role.
0.5
0.6
400,000
200,000
0
0
1
2
3
4
5
6
7
8
9
10
Generation
Notes. Parameter values: N = 1000000, i = 38661, pA = 01; mean number of people sent to by seeds = 259; mean number of people sent to by
nonseeds = 302.
Figure 6(c)
Generational Reach with Varying Mean Probability of
Forwarding (Small World Network)
Sensitivity analysis varying mean probability
of forwarding within a small world network
1,000,000
0.2
0.3
0.4
800,000
Reach
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600,000
0.5
0.6
400,000
200,000
0
0
1
2
3
4
5
6
7
8
9
10
Generation
Notes. Parameter values: N = 1000000, i = 38661, r = 01, pA = 01;
mean number of people sent to by seeds = 259; mean number of people
sent to by nonseeds = 302.
the structure of the network is scale-free, especially
at the higher end of the range of values used as
shown in Figure 8(a). This can be explained by
Bampo et al.: The Effects of the Social Structure of Digital Networks on Viral Marketing Performance
286
Information Systems Research 19(3), pp. 273–290, © 2008 INFORMS
Figure 7
Simulated Eventual Reach with Varying the Probability of
Forwarding (Random, Small World, Scale-Free Networks)
Eventual reach
Random
SF
SW
6.
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
Probability of forwarding
Notes. Parameter values: N = 1000000, i = 38661, r = 01, pA = 01;
mean number of people sent to by seeds = 259; mean number of people
sent to by nonseeds = 302.
the large hubs existing within a simulated scalefree network. This latter point is illustrated more
clearly in Figure 8(b), where the reach is presented
Figure 8(a)
Simulated Eventual Reach with Varying Numbers of
Activated Links Per Node (Random, Small World,
Scale-Free Networks)
Simulated reach
Sensitivity analysis varying the average number of
activated links per node
148,500
138,500
128,500
118,500
108,500
98,500
88,500
78,500
68,500
58,500
Random
Small world
Scale-free
0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
Average number of links per node
4.0
4.5
Notes. Parameter values: N = 1000000, i = 38661, r = 01, pA = 01, pF
(seeds) = 0265, pF (nonseeds) = 0126.
Figure 8(b)
Generational Reach with Varying Numbers of Activated
Links Per Node (Random, Small World, Scale-Free
Networks)
Sensitivity analysis varying the average number of
activated links per node
160,000
140,000
Reach
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Sensitivity analysis
900,000
800,000
700,000
600,000
500,000
400,000
300,000
200,000
100,000
0
generation by generation. This set of results also
shows that an average of four activated links per
node are needed within random and small world
networks to achieve the reach achieved with an
average of two activated links within a scale-free
network.
120,000
100,000
80,000
60,000
2 (Rand)
2 (SW)
2 (SF)
40,000
20,000
4 (Rand)
4 (SW)
4 (SF)
0
0
1
2
3
4
5
6
Generation
7
8
9
10
Notes. Parameter values: N = 1000000, i = 38661, r = 01, pA = 01, pF
(seeds) = 0265, pF (nonseeds) = 0126.
Theory Building and Managerial
Implications
6.1. Implications for Campaigns in General
Campaign managers need guidance in adapting their
marketing strategies in online contexts (Dellarocas
2003). They generally have no way of knowing what
kind of social or digital network structures they are
working with. Our findings show that social network
structures have a significant impact on campaign performance. In particular, we show that scale-free networks are very efficient for viral campaigns and thus
encourage campaign managers to try to capture scalefree properties in their target audience—possibly
through identifying and seeding influential customers
who might then function as hubs. Further, we detect
little differences between small world and random
networks (even with rewiring parameter r = 01). It
appears that clustered networks are not particularly
efficient. Small world networks present a more difficult scenario for the campaign manager because high
clustering generally tempers the spread of the message. Future research should consider mechanisms for
managing areas with poor spread by reinforcement
seeding.
Building on Godes and Mayzlin (2004), we find that
in general the reach is proportional to the number of
seeds used. When using a high initial number of seeds,
the structure of the digital network is less important
(see Figure 5(b)), but at lower levels of initial seeding, the network structure has a marked impact (see
Figure 5(a)). We find that an increase of one activated
contact per person has an appreciable impact on the
campaign—this is especially so with scale-free network (see Figures 8(a) and 8(b)). Our sensitivity analysis shows that the reach is quite sensitive to changes
in the number of activated contacts. This is particularly true with scale-free networks. In fact, we find
that if the empirical campaign managed to increase
the average number of activated contacts per person
Bampo et al.: The Effects of the Social Structure of Digital Networks on Viral Marketing Performance
287
by one, from the observed value of 2.8, the related
incremental increase in reach would be over 30,000.
In contrast, given the low growth rate of seeds in this
campaign, to achieve the same increase in reach, the
company would have needed to seed between 15,000–
20,000 more people. In practical terms, the impact
of a viral marketing campaign is due to messages
being received from friends and acquaintances and
not from mass marketing. The initial seeds are not
the target of a viral campaign—their buying intentions
may not be strongly influenced by a campaign because
they have not received the message from a friend
or acquaintance. In short, the target of the campaign
manager is strong growth, not massive seeding. There
is also a cost trade-off between the acquisition costs
of additional seeds versus the costs associated with
whatever incentives one embeds in the campaign. As
inferred from the simulated results, an incentive that
increased the mean probability of forwarding in the
actual campaign from 0.5 to 0.6 would have resulted
in an increase in reach of over 10,000.
The characteristics of the message and creative execution play an equally important role in determining
a recipient’s propensity to forward the communication, the average number of activated connections,
and, hence, the average number of transmissions. For
example, a humorous advertisement is likely to be
transmitted in much the same manner as jokes are
e-mailed within social networks. The other way to
increase the success of a viral campaign is to introduce a tangible promotional incentive and link it to
behaviors that increase p and . However, one could
also argue that incentives have the potential for the
campaign to extend outside the desired target market. While this is not necessarily a bad thing per se
and there is no financial wastage involved, it has the
potential to inflate campaign performance statistics
and thus overstate the success of the campaign. There
is evidence of this in the empirical data.
can use the first few generations of a campaign as a
learning platform to decode the underlying network
structure and estimate the transmission behavior of
the target audience, and then use this knowledge to
intervene or reshape the campaign strategy for the
later generations. Alternatively, a test campaign could
be run to identify the appropriate network model,
calibrate its parameters and forecast actual campaign
performance, and then modify campaign strategy
accordingly. For example, if the campaign manager
wanted to achieve a better penetration of the target
audience than predicted, the strategic options available would include increasing the number of seeds
used and/or modifying the reward to influence the
transmission behavior.
The General Motors campaign had two distinct
phases of seeding with the initial phase starting in
September and the subsequent, larger phase (comprising 86% of the total number of seeds who participated
in the campaign) commencing a few months later.
Hence, the “test campaign” approach is one that could
have been used by the campaign manager to assess the
performance of the second phase on the basis of learnings from the first phase. For example, if the predicted
reach was deemed too small, the number of seeds
could be increased or actions could be taken to encourage more people to forward the message and/or forward the message to more people. In addition, if this
approach had been used for the General Motors campaign, the analysis could have revealed the limiting
impact that the Web interface appears to have had on
their campaign as discussed §4.2.
Figure 9 compares the simulated second phase for
the campaign based on a random network to what
Figure 9
Using the Learnings from the First Phase of the General
Motors Campaign to Forecast the Second Phase of the
Campaign
Learning from the early part of the campaign
6.2. Implications for Specific Campaigns
To this point, we have simulated three different network structures and identified both random and small
world networks as providing an adequate fit to the
actual campaign. In addition to using the simulation
to deconstruct campaigns and develop insights, this
methodology holds the promise of providing predictive modelling. For example, a campaign manager
Generational growth
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35,000
Actual
Random
30,000
25,000
20,000
15,000
10,000
5,000
0
g0
g1
g2
g3
Generation
g4
g5
g6
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actually transpired. As shown, the fit is generally
good and the total reach forecast by the simulated
campaign is only 3% below the actual. The major
contributor to this disparity is at the first generation.
Analysis of the second phase of the campaign shows
that the group of seeds that initiated this part of the
campaign were even more active (higher forwarding
probability and connectivity) than those in the first
phase. This behavior could be explained by the fact
that the deadline for the competition (and associated
reward) was much closer.
7.
Conclusions and Directions for
Future Research
We began this study with three research objectives
in mind: first, to understand the mediating effects of
differing social network structures on viral marketing
campaign performance; second, to develop a process
for modelling viral marketing campaigns and then
to validate the different models using empirical data;
and third, to conduct a number of simulation experiments to predict the spread of a viral message within
different kinds of social network structures under
different assumptions and scenarios for the empirical
campaign, and show how a campaign manager can
build and apply a learning platform based upon early
performance of the campaign.
In exploring the impact that social network structures have on campaign dynamics, we have provided
managers with useful approaches for optimising the
success of a viral campaign. Specifically, our contributions are threefold. First, we propose a conceptual
framework for digital social networks that differentiates between the underlying social network and
the activated digital network. Second, we illustrate
the impact that network structure, connectivity, and
campaign design have on campaign performance. In
particular, we demonstrate the effect of varying the
number of seeds, the probability of forwarding, and
the number of contacts the message is forwarded to.
Third, and most importantly, the models in this article
provide a basis for quantifying the impact of campaign management inputs and how the analysis can
be used as learnings for managerial decision making. The subtle differences between the network models also provide the essential basis for monitoring
a campaign and determining whether it is performing as expected or whether further input is needed.
The marketing challenge is to achieve enough seeds
and a high enough “epidemic threshold” (which is
impacted through the combined effect of the activation and forwarding) to achieve campaign objectives without the unnecessary expense and possibly
negative impact of flooding the target population
(mass marketing). The models developed here provide a sound basis for campaign managers to meet
this challenge.
These models and simulations provide the first
solid method for measuring the impact of viral promotional activities on the campaign audience’s behavior. With the tools introduced in this paper, it will
become possible to analyse the results of campaigns
and to produce a mathematically supported measure
of the actual forwarding probability of audience members. This provides a basis for scientifically relating
promotional activities to the audience’s probabilities
of forwarding and, thus, reaching a balance between
promotion costs and the size of audience reached by
a campaign.
In addition to our already-stated theoretical and
managerial contributions, this paper also presents
considerable opportunities for future research. The
set of models we have chosen to test is obviously
not exhaustive. It is possible, for example, to reproduce the viral campaign with a model based on a
random network and susceptible-immune-susceptible
(SIS) behavior (in such a model, when members
receive the message, they move into a temporary
state of immunity from further communications but
become susceptible again at a later time). Initial investigations of a wider set of models show that modelling a campaign is no simple task. With a wider
set of models, it is possible to devise quite different models which match the same set of campaign
data. It will, therefore, become increasingly important
to measure statistics that distinguish between different types of model, for example, between SIR and
SIS behavior. Further, it is conceivable that a social
network could be a hybrid of connectivity models;
for example, the underlying social network structure
may have random connectivity while the activated
digital network may display structured (small world
or scale freeness) connectivity. In this study, we used
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the concept of a “generation” as a temporal measure. It would be useful to understand the relationship between time (duration) and generation, as a
time-based analysis would offer further insight from
a managerial perspective.
Further investigation on the mutual influence of
the network and behavior models presented here
is also required. Our current models do not take
into account the notion of receptivity: All individuals reached by the viral message are assumed to
assimilate it. Is this a valid assumption? Marketing
studies have pointed out the existence of special people, called “infuentials” (Keller and Berry 2003), or
“salesmen” (Gladwell 2000) who are extraordinarily
effective in persuading other people to adopt an idea.
How can their effect on viral process’ dynamics be
modelled? What precise role do they play in message
diffusion? How much effort should marketers spend
trying to locate them and factor them into campaign
plans?
In closing, we offer three specific directions for
further research. First, we see a need for more
sophisticated and targeted seeding experimentation.
In particular, a better understanding of the role of
hubs in seeding strategies is needed, as these special individuals have considerably higher connectivity than others (Gladwell 2000, Granovetter 1983)
and, through identification and targeting as campaign
seeds, can be successfully exploited to increase the
success of the campaign. Second, a related avenue
for further enquiry is to consider the effect of possible managerial interventions during a campaign.
Viral marketing has hitherto been portrayed as a random, ground-up phenomenon over which marketers
have little control (Dobele et al. 2005). We disagree
with this contention and believe that further empirical
and experimental research with real campaigns will
unearth opportunities for astute managers to proactively resurrect underperforming campaigns. Conversely, there may even be occasions where a manager
needs to restrict an overly successful viral campaign,
for example, one linked to an expensive sales promotion such as free samples or coupons. Finally, there is
a need for further research into the aesthetic, creative,
and technical components of campaign design. A viral
campaign can be designed in one of two ways: Either
the message is forwarded directly between audience
289
members using e-mail, for example, or via a centralised system where an e-mail embedded link channels recipients through a Web interface. The empirical
campaign discussed in this study is an example of the
latter strategy. An inherent benefit of this strategy is
that it enables the manager to monitor the campaign’s
progress and control the process’ dynamics. An additional benefit of this two-stage approach is that recipients effectively self-screen, thereby reducing wastage
(e.g., generating unsolicited spam). This system also
enables the manager to revive a flagging campaign.
A key benefit of employing a Web interface is that it
can be used to produce an image of the underlying
social network of the target audience. As shown in
this study, the network structure plays a key role in
how a campaign should be managed.
Acknowledgments
The authors sincerely thank Peter Wicki, eBusiness Manager, General Motors Holden Australia, for help with this
project and for access to viral marketing campaign data.
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